A spatially explicit crop planting initiation and progression model for the conterminous United States
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2017 Elsevier B.V. The ability to accurately estimate crop planting date and planting progression has major implications in crop management, crop model applications, and in developing adaptation strategies for future climate change. The objectives of this study are: 1) identify major factors that determine planting initiation and progression of six major crops in the U.S. and 2) develop a spatially explicit planting initiation and progression model. The crops that were evaluated are maize (Zea mays), cotton (Gossypium hirsutum), rice (Oryza sativa), sorghum (Sorghum bicolor), soybean (Glycine max), and winter wheat (Triticum aestivum). County-level daily planting data from 2005 to 2015 for representative states were obtained from USDA Risk Management Agency. For the five summer crops, the earliest planting gradually shifts to later dates with increasing latitude and elevation. The trend is reversed for winter wheat, with planting initiation shifting to earlier dates from south to north and from low to high elevation. A minimum planting temperature threshold was established for the five summer crops, which decreases from south to north and from low to high elevation. A maximum planting temperature threshold was established for winter wheat, which decreases from south to north but increases from low to high elevation. A spatially explicit temperature model as a function of latitude, longitude and elevation was established to predict planting initiation, while a soil texture-based soil wetness index predicts planting delays due to excessive precipitation. The model was calibrated with 20052009 data and validated with 20102015 data; it provided sound goodness of fit for planting initiation and weekly planting progression. The spatially explicit model for planting initiation and progression could be used to guide crop production planning and to improve the planting date and progression algorithms in crop models for regional simulation analysis.
